Optical Character Recognition (OCR) is the mechanical or electronic conversion of scanned images of handwritten, typewritten or printed text into machine-encoded text. Touching characters are always difficult to be recognized by an OCR engine. The binarized output of a TV video frame includes various noises like salt and pepper noise which represents itself as randomly occurring white and black pixels. Thus, there is a high possibility of false detection of characters due to the presence of this type of noise.
There are various methods available for touching character segmentation of texts obtained from videos. The main limitation of the available methods is that it cannot perform well for all text rich videos in the corpus. The existing method assumes that all characters are of almost equal width and thus the candidate cutting positions are not properly obtained thereby leading to an over segmented character. The existing methods consist of a possibility of segmentation at the left/right extension portion of the characters like “T” or “E”. Moreover, the inventions are related to texts obtained from videos only.
Therefore, there is a need of a suitable touching character segmentation method which would help in improving the recognition accuracy of an OCR engine by calculating the exact touching position and segmenting the touching characters at that point only. Also, the method should be capable of being applied to text output obtained from both images and videos.